Liquid AI
"The future of AI is local."
MIT-spinout building device-native foundation models (LFMs) that run on-device across phones, cars, robots and edge hardware.
Liquid AI, Inc. is a U.S. foundation-model company founded in 2023 as a spin-off from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) by Ramin Hasani, Mathias Lechner, Alexander Amini and Daniela Rus. The company builds Liquid Foundation Models (LFMs) - efficient, general-purpose AI models designed to run natively on processors outside of data centers, addressing the latency, privacy and hardware constraints of the physical world.
Its technology traces back to MIT research on liquid neural networks and state-space architectures. Liquid AI ships compact, hardware-aware models (including the LFM2 and LFM2.5 families, vision-language and reasoning variants, and Mixture-of-Experts models) that run on common runtimes such as llama.cpp, MLX, ONNX, CoreML, SGLang and vLLM, with some models under 1GB. Developers can fine-tune and deploy models through the Liquid Edge AI Platform (LEAP) and the Apollo tooling. For industrial and robotics use, Liquid markets memory-efficient models that run directly on edge CPUs, NPUs and GPUs to bring human-level reasoning to robots and sensors, alongside agentic orchestration across SCADA, PLC and MES systems. The company reports 40+ LFMs shipped and tens of millions of model downloads, and has announced partnerships with Mercedes-Benz, AMD, Qualcomm, Shopify and Insilico Medicine. Liquid AI operates Liquid Labs as its frontier-research arm.
Primary type & automation activities this supplier delivers:
Liquid Foundation Models (LFM2.5)
- Model: LFM2.5
- Launch Year: 2026
On-device foundation model family for reasoning, vision-language and retrieval, some variants under 1GB.
Liquid Edge AI Platform (LEAP)
SDK to fine-tune, bake to any runtime and deploy LFMs to edge devices in minutes.
Company Facts
Founded
2023
Primary Role
Software/Algorithm
Company Size
-
Primary Region
North America
Annual Sales
-
Funding Stage
-
Funding Total
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Certifications & Memberships
Harmonic Drive to Present on Integrated Actuators at RoboBusiness 2026
At RoboBusiness 2026, Marcel Almonte from Harmonic Drive will lead a session on integrated drive actuator design. Scheduled for October 20 at 10:30 a.m. PT in Santa Clara, California, the session will explore the benefits of integrated actuators in reducing engineering complexity and enhancing system performance. This presentation is significant as it addresses the critical decision-making process in selecting motion architectures for robotics. Attendees will learn how integrated actuators can streamline system integration, reduce cabling complexity, and accelerate time-to-market, ultimately leading to more efficient robotic designs. Participants should watch for practical insights on selecting motion solutions tailored to specific application requirements and constraints. No further timeline was disclosed at the time of publication.
Actuators / Motors / Servos Events Motion Control News Robot Components Technologies
Elmo Motion Control Introduces Innovative Titanium and Platinum Servo Solutions
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Transforming Industrial Maintenance: The Role of Agentic and Physical AI in Repair Efficiency
The industrial sector has long relied on predictive maintenance to foresee equipment failures, but this approach often leads to administrative bottlenecks. Alerts generated by AI models trigger slow, manual processes that hinder timely repairs. To overcome these challenges, industries are shifting towards a model that incorporates agentic AI and physical AI, enabling autonomous orchestration of logistics and execution of repairs. Agentic AI allows for the autonomous management of maintenance tasks, optimizing scheduling and logistics without human intervention. This transition is crucial for enhancing operational efficiency, as it transforms maintenance from a reactive process into a self-optimizing engine. For instance, in high-volume manufacturing, agentic AI can immediately address issues flagged by predictive models, significantly reducing downtime and improving return on investment. Looking ahead, the integration of agentic and physical AI is set to redefine maintenance operations. As organizations adopt these technologies, they must ensure safety through deterministic bounding to mitigate risks associated with autonomous decision-making. The future of industrial maintenance will likely see a seamless blend of AI-driven logistics and physical execution, paving the way for more efficient and effective repair processes.
Factory / Analytics
Exploring Custom Motor Solutions and Advanced Motion Control Technologies
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Automation Solutions for the Future of Sheet Metal Processing
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Allgemein
Teradyne Launches Titan HP Platform with Enhanced Burn-In for AI Data Center Devices
Teradyne, Inc. has unveiled its Titan™ HP platform, which features advanced burn-in capabilities tailored for high-power AI and automotive devices. This innovative system allows for per-device thermal control and realistic workload testing, optimizing reliability stress functions and reducing costs associated with testing and time to market. The introduction of the Titan HP platform is significant as it addresses the increasing demand for high-reliability testing in AI silicon, akin to standards in the automotive sector. By enabling system-level reliability stress testing, Teradyne aims to enhance the quality of shipped devices while managing capital costs and manufacturing space constraints. Looking ahead, Teradyne's Titan HP is positioned to support the evolving power requirements of future AI accelerators. The platform is already available for production use, with demonstrations scheduled at upcoming industry events, including SEMICON West and the IEEE International Test Conference.
Understanding Connector Failures in Robot Cabling: M8 and M12 Selection for Motion Applications
The article discusses the critical factors affecting the performance of M8 and M12 connectors in robotic applications. It emphasizes that while connectors may meet datasheet specifications, their performance in real-world motion scenarios is often overlooked. This oversight can lead to recurring failures in robotic systems, particularly when connectors are subjected to mechanical loads during operation. Understanding the dynamics of robot movement is essential for ensuring reliable connections. The article highlights that connectors and cables must be evaluated as part of a complete moving system, considering factors such as cable length, support points, and the mechanical loads introduced by the robot's motion. Failure to address these issues can result in intermittent faults that persist even after component replacement. Looking ahead, it is crucial for engineers to assess the entire motion envelope of robotic systems when selecting connectors. This includes evaluating the impact of cable routing and support on connector performance. No further timeline was disclosed at the time of publication.
Doosan Robotics Selected for Two Government-Funded R&D Projects in Physical AI
Doosan Robotics has been chosen for two government-funded R&D projects aimed at advancing Physical AI technologies. Announced on October 6, the projects focus on a next-generation collaborative robot and an intelligent welding solution, with a combined budget of approximately KRW 98.9 billion, including KRW 68.1 billion in government funding. These initiatives are significant as they aim to enhance robot capabilities in perception, decision-making, and control, which are crucial for high-complexity manufacturing processes. The first project will develop collaborative robots that can operate independently using AI semiconductors, while the second will automate complex welding tasks, particularly in nuclear power equipment manufacturing. Looking ahead, Doosan Robotics plans to commercialize these technologies by 2031, targeting industries where data security and communication latency are critical. The projects will run for 54 and 45 months, respectively, with various academic and industry partners involved, reflecting a strategic move to bolster Korea's manufacturing competitiveness through advanced robotics.
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Liquid AI Launches LFM2.5-2.6B Model for Local AI Applications on Small Devices
Liquid AI, a startup founded by former MIT computer scientists in 2023, has introduced LFM2.5-2.6B, a new open-weight language model tailored for agentic workloads. This model can operate entirely on local hardware, including devices as small as a Raspberry Pi, without the need for cloud inference or GPUs, making it ideal for enterprises in regulated sectors that handle sensitive data. The significance of LFM2.5-2.6B lies in its ability to support high-volume, defined tasks such as document management and workflow automation in environments with limited connectivity. The model contains 2.6 billion parameters and features a 128,000-token context window, allowing it to perform efficiently on consumer hardware while maintaining low operational costs. Looking ahead, Liquid AI's focus on edge AI applications may reshape how enterprises approach AI deployment, particularly in scenarios where latency and privacy are critical. No further timeline was disclosed at the time of publication.
Technology
Physical AI Landscape: From Digital Intelligence to the Embodied Physical World
A comprehensive industry analysis of Physical AI — covering Embodied AI, Field AI, Factory AI, VLM, World Model, VLA, and the VLA vs WAM architecture debate. Includes 20+ companies, 28 references, and the RobotToday Physical AI Landscape diagram. July 2026.
Liquid AI's smallest model yet LFM2.5-230M beats models 4X its size at data extraction, can run 'anywhere'
Liquid AI, a company founded by former MIT computer scientists, has unveiled its latest AI language model, LFM2.5-230M, which is designed for efficient data extraction and local deployment on devices such as smartphones and laptops. Released today, this 230-million-parameter model is noted for its ability to run on various hardware platforms, outperforming larger models like Alibaba's Qwen3.5 and Google's Gemma 3 in specific benchmarks. Targeting developers and engineers, LFM2.5-230M operates under a dual-use commercial license, allowing free access for individuals and companies with annual revenues below $10 million, while larger enterprises must secure a paid agreement. The model distinguishes itself by utilizing the LFM2 architecture, enabling high inference speeds with a minimal memory footprint, making it suitable for edge computing. Liquid AI's launch reflects a broader industry shift towards architectural efficiency rather than sheer parameter counts, as major AI firms focus on models with hundreds of billions of parameters. The LFM2.5-230M is specifically tailored for lightweight data extraction tasks, allowing businesses to automate processes without relying on costly cloud services. In practical applications, the model has been successfully deployed in a humanoid robot, demonstrating its capability to process complex commands efficiently. Available immediately on platforms like Hugging Face, LFM2.5-230M aims to revolutionize how enterprises manage data extraction, moving away from traditional, rigid systems to more adaptable AI-driven solutions.
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